Rapid prediction of in-hospital mortality among adults with COVID-19 disease.
<h4>Background</h4>We developed a simple tool to estimate the probability of dying from acute COVID-19 illness only with readily available assessments at initial admission.<h4>Methods</h4>This retrospective study included 13,190 racially and ethnically diverse adults admitted...
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0269813 |
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| author | Kyoung Min Kim Daniel S Evans Jessica Jacobson Xiaqing Jiang Warren Browner Steven R Cummings |
| author_facet | Kyoung Min Kim Daniel S Evans Jessica Jacobson Xiaqing Jiang Warren Browner Steven R Cummings |
| author_sort | Kyoung Min Kim |
| collection | DOAJ |
| description | <h4>Background</h4>We developed a simple tool to estimate the probability of dying from acute COVID-19 illness only with readily available assessments at initial admission.<h4>Methods</h4>This retrospective study included 13,190 racially and ethnically diverse adults admitted to one of the New York City Health + Hospitals (NYC H+H) system for COVID-19 illness between March 1 and June 30, 2020. Demographic characteristics, simple vital signs and routine clinical laboratory tests were collected from the electronic medical records. A clinical prediction model to estimate the risk of dying during the hospitalization were developed.<h4>Results</h4>Mean age (interquartile range) was 58 (45-72) years; 5421 (41%) were women, 5258 were Latinx (40%), 3805 Black (29%), 1168 White (9%), and 2959 Other (22%). During hospitalization, 2,875 were (22%) died. Using separate test and validation samples, machine learning (Gradient Boosted Decision Trees) identified eight variables-oxygen saturation, respiratory rate, systolic and diastolic blood pressures, pulse rate, blood urea nitrogen level, age and creatinine-that predicted mortality, with an area under the ROC curve (AUC) of 94%. A score based on these variables classified 5,677 (46%) as low risk (a score of 0) who had 0.8% (95% confidence interval, 0.5-1.0%) risk of dying, and 674 (5.4%) as high-risk (score ≥ 12 points) who had a 97.6% (96.5-98.8%) risk of dying; the remainder had intermediate risks. A risk calculator is available online at https://danielevanslab.shinyapps.io/Covid_mortality/.<h4>Conclusions</h4>In a diverse population of hospitalized patients with COVID-19 illness, a clinical prediction model using a few readily available vital signs reflecting the severity of disease may precisely predict in-hospital mortality in diverse populations and can rapidly assist decisions to prioritize admissions and intensive care. |
| format | Article |
| id | doaj-art-9c5474dc05b24a7d8c165f0326b7a034 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-9c5474dc05b24a7d8c165f0326b7a0342025-08-20T03:23:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01177e026981310.1371/journal.pone.0269813Rapid prediction of in-hospital mortality among adults with COVID-19 disease.Kyoung Min KimDaniel S EvansJessica JacobsonXiaqing JiangWarren BrownerSteven R Cummings<h4>Background</h4>We developed a simple tool to estimate the probability of dying from acute COVID-19 illness only with readily available assessments at initial admission.<h4>Methods</h4>This retrospective study included 13,190 racially and ethnically diverse adults admitted to one of the New York City Health + Hospitals (NYC H+H) system for COVID-19 illness between March 1 and June 30, 2020. Demographic characteristics, simple vital signs and routine clinical laboratory tests were collected from the electronic medical records. A clinical prediction model to estimate the risk of dying during the hospitalization were developed.<h4>Results</h4>Mean age (interquartile range) was 58 (45-72) years; 5421 (41%) were women, 5258 were Latinx (40%), 3805 Black (29%), 1168 White (9%), and 2959 Other (22%). During hospitalization, 2,875 were (22%) died. Using separate test and validation samples, machine learning (Gradient Boosted Decision Trees) identified eight variables-oxygen saturation, respiratory rate, systolic and diastolic blood pressures, pulse rate, blood urea nitrogen level, age and creatinine-that predicted mortality, with an area under the ROC curve (AUC) of 94%. A score based on these variables classified 5,677 (46%) as low risk (a score of 0) who had 0.8% (95% confidence interval, 0.5-1.0%) risk of dying, and 674 (5.4%) as high-risk (score ≥ 12 points) who had a 97.6% (96.5-98.8%) risk of dying; the remainder had intermediate risks. A risk calculator is available online at https://danielevanslab.shinyapps.io/Covid_mortality/.<h4>Conclusions</h4>In a diverse population of hospitalized patients with COVID-19 illness, a clinical prediction model using a few readily available vital signs reflecting the severity of disease may precisely predict in-hospital mortality in diverse populations and can rapidly assist decisions to prioritize admissions and intensive care.https://doi.org/10.1371/journal.pone.0269813 |
| spellingShingle | Kyoung Min Kim Daniel S Evans Jessica Jacobson Xiaqing Jiang Warren Browner Steven R Cummings Rapid prediction of in-hospital mortality among adults with COVID-19 disease. PLoS ONE |
| title | Rapid prediction of in-hospital mortality among adults with COVID-19 disease. |
| title_full | Rapid prediction of in-hospital mortality among adults with COVID-19 disease. |
| title_fullStr | Rapid prediction of in-hospital mortality among adults with COVID-19 disease. |
| title_full_unstemmed | Rapid prediction of in-hospital mortality among adults with COVID-19 disease. |
| title_short | Rapid prediction of in-hospital mortality among adults with COVID-19 disease. |
| title_sort | rapid prediction of in hospital mortality among adults with covid 19 disease |
| url | https://doi.org/10.1371/journal.pone.0269813 |
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